306 citations · 682 across the 25 of their papers we have counts for
Showing 2017Show all
3 papers · 1 filter
stat.ML2017★ 6 cited
Stein Variational Message Passing for Continuous Graphical Models
Dilin Wang, Zhe Zeng, Qiang Liu
We propose a novel distributed inference algorithm for continuous graphical models, by extending Stein variational gradient descent (SVGD) to leverage the Markov dependency structu…
stat.ML2017★ 15 cited
Learning Deep Energy Models: Contrastive Divergence vs. Amortized MLE
Qiang Liu, Dilin Wang
We propose a number of new algorithms for learning deep energy models and demonstrate their properties. We show that our SteinCD performs well in term of test likelihood, while Ste…
stat.ML2017★ 29 cited
Learning to Draw Samples with Amortized Stein Variational Gradient Descent
Yihao Feng, Dilin Wang, Qiang Liu
We propose a simple algorithm to train stochastic neural networks to draw samples from given target distributions for probabilistic inference. Our method is based on iteratively ad…